Use when user input is vague, underspecified, lacks boundaries or acceptance criteria, or would benefit from being reframed into a more executable prompt before work begins. Also use when user explicitly asks to optimize/refine/improve a prompt.

Use when user input is vague, underspecified, lacks boundaries or acceptance criteria, or would benefit from being reframed into a more executable prompt before work begins. Also use when user explicitly asks to optimize/refine/improve a prompt.

prompt-refiner

A prompt is not just wording polish — it is task clarification, boundary setting, and verification shaping.

Refine vague user prompts into clear, actionable, verifiable versions. Show the refined result and let the user confirm before execution. Works as a closed loop with session-learner, which captures user choice preferences over time.

Quick Reference

Situation	Action
Vague request	Refine first
Complete prompt	Execute directly, no popup
Substantial improvement	Show refined + popup choose
No substantial improvement	Skip popup, execute original
User says "just do it"	Auto-apply
User says "only optimize"	Return refined, don't execute

Continue Modes

1. Popup-confirm (default) — Show refined prompt, popup to choose refined vs original, execute after choice
2. Auto-apply — When user says "just do it / skip confirmation", show refined then execute immediately
3. Optimize-only — When user only asks to refine without executing, return refined result only

When NOT to Use

• User input is already well-structured with clear goals, constraints, and acceptance criteria
• Single-step operations (delete a line, rename a variable, change a single string)
• Simple factual questions or explanations
• User explicitly says "don't optimize" or "just do it"

Workflow

1. Extract original prompt and current session context (goals, constraints, tech stack, errors, expected output).
2. Generate refined prompt, ensuring:
   • Core intent unchanged
   • Goals and boundaries explicit
   • Output format verifiable
   • Language concise, no fluff
3. Produce result based on Continue Mode.
4. After user makes a choice, generate a learning signal for session-learner — capture preference pattern, never record full prompt text.

Refined Prompt Structure

Include the following blocks as needed (trim, don't mechanically stack):

• Goal
• Context
• Constraints / Non-goals
• Execution Requirements
• Output Format
• Acceptance Criteria

For detailed patterns and examples, see references/prompt-patterns.md.

Output Templates

A) Popup-confirm (default)

First, judge whether refinement adds real value. If the refined prompt only tweaks wording without adding explicit goals/constraints/output format/acceptance criteria, and doesn't significantly reduce ambiguity, it has no substantial optimization value.

• No substantial value: Don't popup, don't interrupt. Execute with the original prompt directly.
• Has substantial value: Execute the two steps below. Do not skip or merge them.

Step 1 (required): Output the refined prompt as plain text in the chat area first.

Optimized Prompt

Step 2 (required, after step 1): Call AskUserQuestion popup for user to choose. Options:

• A: Use refined prompt to continue (recommended)
• B: Use original prompt to continue

Forbidden:

• Do not popup without showing the refined prompt content first
• Do not put the refined prompt inside AskUserQuestion description as a substitute for step 1
• Steps 1 and 2 must be in the same response

Execute after user chooses.

B) Auto-apply

Optimized Prompt

---

Then execute immediately.

C) Optimize-only

Optimized Prompt

Do not execute the task.

Heuristics

• Rewrite vague phrases ("optimize it / make it better / fix it up") into actionable steps with clear criteria.
• Fill in missing inputs, boundary conditions, and completion definitions.
• For code tasks: specify scope, verification method, expected deliverables.
• For content tasks: specify audience, tone, length, structure constraints.
• If critical context is missing, ask the minimum necessary clarification; otherwise proceed.

Integration with session-learner

• When user chooses refined or rejects it, this is a preference signal for session-learner.
• session-learner should only summarize rules (e.g., "user prefers seeing refined version before confirming"), never record full prompt text.
• Over time, session-learner builds a preference profile that makes prompt-refiner increasingly aligned with user habits.

Priority Rules

• When user explicitly specifies a flow, follow user instructions.
• When no explicit instruction, use Popup-confirm.